Berlin

Berlin has approximately 900 schools. Until now, traffic safety assessments by road authorities have focused mainly on the immediate surroundings of schools rather than on the routes leading to them. Initial efforts to identify highly frequented school routes have commenced under new legislation but focus on resource-intensive methods like on-site inspections and consultations with individual schools. 

Therefore, the city’s goal is now to establish a structured, standardised and more efficient procedure. This will enable children, parents, police units, and local authorities to make objective and comparable decisions for implementing safety measures. 

The new legal legislation introduced in Germany about six months ago, allows municipalities to impose speed limits (on main and on-side roads) on heavily frequented school routes, yet no standardised method for applying this rule exists nationwide. However, there is a need to implement a clear, data-driven process to support evidence-based decisions and faster implementation. 

The challenge lies not only in analysing existing data, such as school locations, infrastructure, accident records, and traffic information, but also in defining which data sources should be included. Privacy concerns, particularly regarding personally reported data, mean that some information cannot be shared publicly. Therefore, the model must include mechanisms to simulate or anonymise sensitive information while maintaining accuracy. The resulting transparent model will provide a reliable basis for authorities to impose speed limits on the most critical school routes. 

Some early initiatives have already laid the groundwork: 

1. In 2022 and 2025, a planning consultancy digitally recorded school routes for two Berlin districts, but this model applied only locally and preceded the new legislation. Furthermore, only primary school pupils who walk to school were considered here, and not secondary school pupils who cycle on the road. 
2. At state level, a project is under development that should enable students to digitally report traffic and infrastructure problems via an app, supporting planning activities by the traffic authority. 

The City of Berlin is now looking for solution providers through the RAPTOR programme who can build upon and complement these existing efforts. The selected pilot should take advantage of the data, experience, and insights already generated at district and state levels, such as the digital mapping of school routes and the student reporting app, to develop a scalable, data-driven methodology for identifying the most frequently used school routes. The focus is on producing an initial analytical model or simulation that consolidates existing datasets and estimates high-frequency routes possibly for the whole city. The solution should support evidence-based decision-making for future speed-limit implementation (main and on-side) roads and be compatible with GIS visualisation tools to assist further analysis by the city’s traffic authority. 

To support this work, several key datasets are already available: 

• Comprehensive infrastructure and road traffic data 

• Locations, catchment areas, and sizes of all schools 

• Recent accident and incident data 

• Existing speed-limit information 

• Bicycle lanes, bus lanes, and related infrastructure layers 

Berlin intends to use the new planning possibilities for safe school routes as quickly as possible and aims to be a pioneer in this field. By creating safer school routes, children and parents will be encouraged to walk or cycle to school, fostering sustainable mobility habits from an early age. 

DESIRED SITUATION

Short-term success indicators will demonstrate the pilot’s effectiveness in enabling faster and more consistent identification of priority school routes.
Proposed metrics include: 

• Kilometers of high-frequency school routes identified  

• Kilometers of these routes located along main traffic roads, where new speed limits could be introduced 

• Number of schools analysed for high-frequency routes during the pilot 

• Time saved per school compared to manual processing methods used previously.